For example, (Hu and Liu, 2004;Nishikawa et al., 2010) have explored opinion summarization in review domain, and (Paul et al., 2010) summarizes contrastive viewpoints in opinionated text.

we choose to investigate these two methods since they have been widely used in text and speech summarization, and perform competitively.


In the graph-based method, we use an adjacency matrix to model the dialogue structure and utilize it to find salient utterances in conversations.


We observed that the major components which shape an article on a contention are quotes from disputants and journalists" commentary.

the false positives were mostly the disputants who appear only a few times both in the article set and the news search results.


Thus, building a domain specific NER for tweets is necessary, which requires a lot of annotated tweets or rules.

the OpenNLP and the Stanford parser (Klein and Manning, 2003) table 1 shows the overall results for the baselines and ours with the name N ER CB .


the misspelling of can't as cant) are outside the scope of this research.

the prefix and suffix features are intended to capture the fact that leading and trailing characters are frequently dropped from words, e.g.


For keyword ranking, we modify the Topical PageRank method proposed by Liu et al.

in this paper, we propose to extract topical keyphrases as one way to summarize Twitter.


β and µ are empirically set to 0.01 and 500.

our work is related to unsupervised keyphrase extraction.


The topic-specific preference value P t (w) for each word w is its random jumping probability with the constraint that w∈V P t (w) = 1 given topic t. A large R t (•) indicates a word is a good candidate keyword in topic t. We denote this original version of the Topical PageRank as TPR.

we also tried the standard LDA model and the author-topic model on our data set and found that our proposed topic model was better or at least comparable in terms of finding meaningful topics.


Language modeling methods (Tomokiyo and Hurst, 2003) and natural language processing techniques (Barker and Cornacchia, 2000) have also been used for unsupervised keyphrase extraction.

user-level tags may not be suitable to summarize the overall Twitter content within a certain period and/or from a certain group of people such as people in the same region.


The weights of the CRF component of our model, θ SEQ , are the only weights learned at training time, using a distant supervision process described in Section 6.

while the two-stage filtering does not fully eliminate noise in the input stream, it greatly reduces the presence of irrelevant messages to a manageable 5,800 messages without filtering too many 'signal' tweets.


These properties of social media streams make existing extraction techniques significantly less effective.

we incorporate this idea into our model by simultaneously clustering output and labeling documents rather than performing the two tasks in serial fashion.


Note that the number of names with more than five transliterations was small.


Furthermore, the features learned by our model reveal interesting characteristics of the language pairs being modeled.

table 4 demonstrates the same pattern of improving results with our alignment model.


Globally normalized discriminative models with latent variables (Quattoni et al., 2004) have been used for a number of language processing problems, including MT (Dyer and Resnik, 2010;Blunsom et al., 2008a).

word association features are at the heart of all lexical translation models, whether generative or discriminative.


These systems used a manually labelled set of data for initial supervised training, which means that they are semi-supervised systems.

this could be done randomly, but it turns out that this does not work well for some tasks.


Results show that using Chart Constrains as a baseline, we prune (skip) 33% of the total chart cells.

we use the same loss function L λ , this time training several classifiers: Note that in Equation 4 when k = 0, we recover the open/closed cell classification of Equation 2, since a beam width of 0 indicates that the chart cell is completely closed.


Accordingly, we use variables x A i ,e j ,l and x A i ,e j ,r , for each e j ∈ E(v i ), to denote the string components in tuples generated by A i (here subscripts l and r indicate left and right positions, respectively; see below).

the fact that variables in queries arising from LCFRS rules correspond to the endpoints of spans in the string to be parsed means that these queries have certain structural properties (Gildea, 2011).


Example 3 Consider the synchronous rule s : The first component of a synchronous rule derived from this can be one of the following eight: The second component can be one of the following six: In total, the transformed grammar will contain 8 * 6 = 48 synchronous rules derived from s. For each synchronous rule s, the above grammar transformation produces O(|s|) left rule components and as many right rule components.

this is not possible in our case, since SCFGs do not allow normal forms with a constant bound on the length of the right-hand side of each component.


M is a parameter that trades-off between approximation quality and problem difficulty.

we find that such methods are unstable on our problem.


Based on human evaluations our models generate summaries with higher linguistic quality in terms of coherence, readability, and redundancy compared to benchmark systems.

; ETTM is capable of capturing focused sentences with general words related to the main concepts of the documents and much less redundant sentences containing concepts specific to user query.


For instance, C i (S) could be query-dependent in how it measures how much query-dependent information in i is covered by S. Also, the coefficient α could be query and sentence dependent, where it takes larger value when a sentence is more relevant to query (i.e., a larger value of α means later truncation, and therefore more possible coverage).

although the authors may not have noticed, their objective functions are also submodular, adding more evidence suggesting that submodularity is natural for summarization tasks.


Then the feature based method explicitly extracts a variety of lexical, syntactic and semantic features for statistical learning, either generative or discriminative (Miller et al., 2000;Kambhatla, 2004;Boschee et al., 2005;Zhou et al., 2005;Jiang and Zhai, 2007).

we study the impact of the size of training data on cluster features and analyze the performance improvements through an extensive experimental study.


For finance, an index change can be annotated as "30 points, or 0.8 percent," while our model identifies "30 points" and "0.8 percent" as separate relations.

while remaining almost unchanged for earthquake.


Clusters of similar in-domain documents are A strong earthquake rocked the Philippine island of Mindoro early Tuesday, [destroying]   increasingly available in forms such as Wikipedia article categories, financial reports, and biographies.

given a collection of news articles about earthquakes, for example, our method discovers relations such as the earthquake's location and resulting damage, and extracts phrases representing the relations' instantiations.


A qualitative analysis of the results indicates that our model often picks up on regularities that are difficult to distinguish without relation-specific supervision.

we require that every token of the corpus occurs at most once as a word in a relation's argument in expectation.


This section describes the generative process, while Sections 4 and 5 discuss declarative constraints.

this prior would need to be adjusted every time the number of documents or feature selection changes; using a constraint is an appealing alternative that is portable across domains.


Conceptually, a world is a relational database where each predicate is a relation (possibly infinite).

the logical forms there can become quite complex, and in the context of program induction, this would lead to an unwieldy search space.


The supervised POS tagging accuracies (on this tagset) are shown in the last row of Table 2.

our monolingual similarity function (for connecting pairs of foreign trigram types) is the same as the one used by Subramanya et al.


In other words, the set of hidden states F was chosen to be the fine set of treebank tags.

for seven out of eight languages a threshold of 0.2 gave the best results for our final model, which indicates that for languages without any validation set, τ = 0.2 can be used.


After running label propagation (LP), we compute tag probabilities for foreign word types x by marginalizing the POS tag distributions of foreign trigrams u i = x − x x + over the left and right context words: We then extract a set of possible tags t x (y) by eliminating labels whose probability is below a threshold value τ : We describe how we choose τ in §6.4.

the number of fine tags varied across languages for our experiments; however, one could as well have fixed the set of HMM states to be a constant across languages, and created one mapping to the universal POS tagset.


Note that f is composed of a likelihood component and an edge prior expressed by P (X uv = 1), which we assume to be some constant.

next, we show how sparsity is exploited to scale the use of ILP solvers.


The table also shows results for the rules from Sherlock k .

employing ILP is an appealing approach for obtaining an optimal solution.


The second challenge is scalability: ILP solvers do not scale well since ILP is an NP-complete problem.

there must be a node u ∈ U and w ∈ W (w.l.o.g) such that for some node v, (u, v) and (v, w) are in E new , but (u, w) is not.


This phrase table is traditionally generated by going through a pipeline of two steps, first generating word (or minimal phrase) alignments, then extracting a phrase table that is consistent with these alignments.

we take a Bayesian approach, integrating over all possible values of the hidden parameters: If θ takes the form of a scored phrase table, we can use traditional methods for phrase-based SMT to find P (e|f , θ) and concentrate on creating a model for P (θ| E, F ).


The heuristically trained baseline takes advantage of 'gap rules' to reorder based on lexical context cues, but makes very limited use of the hierarchical structure above the lexical surface.

we employ the induced probabilistic HR-SCFG G as the backbone of a log-linear, feature based translation model, with the derivation probability p(D) under the grammar estimate being one of the features.


a J , which describe the mapping from a word position j in queried question to a document word position i = a j .

given a returned result by VSM, an annotator is asked to label it with "relevant" or "irrelevant".


As pointed out by Berger and Lafferty (1999) and , document-query translation requires a distillation of the document, while translation of natural language tolerates little being thrown away.

(2008), the ranking function can be written as: where P (w|t) denotes the translation probability from word t to word w. Xue et al.


To avoid zero probability, we use Jelinek-Mercer smoothing (Zhai and Lafferty, 2001) due to its good performance and cheap computational cost.

using much longer phrases in the phrase-based translation model does not seem to produce significantly better performance (row 8 and row 9 vs. row 10 are not statistically significant).


This corpus allows us to efficiently access huge amounts of web-derived information in a compressed way, though in the process it limits us to local queries.

one challenge with this approach is that an external search API is now embedded into the parser, raising issues of both speed and daily query limits, especially if all possible attachments trigger queries.


An advantage of FLoss is that it and its derivatives with respect to w (which are required for numerical optimisation) are easy to calculate exactly.

the log figure of merit is the sum of the log language model probability and the log channel model probability plus 1.5 times the number of edits in the sentence.


• Statistics 7: minimum, maximum, range, mean, standard deviation, skewness and kurtosis value.

the use of prosody in speech understanding applications has been quite extensive.


In practice, we need to weight them differently, therefore, the combined score for a hypothesis W is: where score ASR (W ) is generated by ASR systems (composed of acoustic and language model scores) and λ is optimized using held out data.

we introduce a weighting factor based on syllable duration (dur(i)).


(2007), in that we train a single model for all codes with code-specific and generic features.

regardless of its simplicity, the algorithm has shown very good performance on a variety of clinical document types.


It also cannot propose codes that have not been seen in the training data as it has no model for these codes.

this is because several of our emission features look at features of other candidate codes.


This is likely due to the different objective function in the corresponding optimization formulations: while the ranking model attempts to ensure a correct ordering between the grades, the regression model seeks to minimize an error objective that is closer to the RMSE.

in certain scenarios, such as a number of sites worldwide with limited teacher availability, online learning environments, and individual or group study sessions done outside of class, an instructor may not be readily available.


We looked at 1.5 million words in total and found approximately 2500-2700 words that were repeated more than 50 times.

slang and punctuation, which excludes the emoticons and acronyms counted in the other features, increased as well, but not as significantly.


's approach using the same data and have a 4% increase in accuracy.

our hypothesis is that significant changes in writing style distinguish pre-social media bloggers from post-social media bloggers.


To create training and test sets, we partitioned the authors of text from the corpus into two sets: A and B.

we refer to these lects as: • UpSpeak: Communication directed to someone with greater social authority.


A baseline classifier that always predicted the majority class would, on its own, achieve an accuracy of 74% on UpSpeak/DownSpeak classification of unweighted test set instances with a minimum length of 500 words.

this total includes emails to non-Enron employees, such as family members and employees of other corporations, emails to multiple people, and emails received from Enron employees without a known corporate role.


The feature spaces are the shallow and rich feature spaces.

they argue that the multiple learner approach is a better choice for CoRe than the multiple view approach of co-training.


In a real application, we do not have access to true markables, so an evaluation on system markables (markables automatically detected by the system) reflects actual expected performance better.

(2002) and Ng and Cardie (2003) is that we do not use any human-labeled coreference pairs.


Even though we train on a labeled corpus, the labeling of the corpus is produced in a completely automatic fashion, without recourse to hu- man labeling.

it is apparent from this table that our supervised and unsupervised self-trained models outperform Relax, SUCRE 2010 and Tanl-1.


(2010) challenges the appropriateness of machine learning methods for CoRe.

we need (i) measures of the quality of system markables (i.e., an evaluation of the markable detection subtask) and CoRe performance on system markables as well as (ii) a measure of CoRe performance on true markables.


Therefore, in the task of zero-anaphora resolution all verbs appearing in a text are considered candidates for zero pronouns, and all gold mentions or system mentions preceding a candidate zero pronoun are considered as candidate antecedents.

dS-CASCAdE: the model first filters out nonanaphoric candidate anaphors using an anaphoricity determination model, then selects an antecedent from a set of candidate antecedents of anaphoric candidate anaphors using an antecedent identification model.


The third variable, z j , encodes the information provided by the parser: it is 1 with cost c S j = −log(P (SU BJ|j)) if the parser thinks that verb j has an explicit subject with probability P (SU BJ|j), otherwise it is 0. subject to The crucial fact about the relation between z j and y j is that a verb has either a syntactically realized NP or a zero pronoun as a subject, but not both.

we considered as pseudo-mentions all bunsetsu chunks (i.e.


Martha Stewart is hoping people don't run out on her.

a natural question is: what kind of world knowledge can we extract from annotated data?


LMBOT and MBOT with finite synchronization are strictly more expressive than STSG and compute classes that are not closed under composition.

the rank of a pre-translation equals the number of output trees stored in it.


Overall, the forest-based translation can reduce the risks from upstream parsing errors and expand the search space, but it cannot sufficiently address the syntactic divergences between various language-pairs.

we'll evaluate model p c (t|γ,m) in Eqn.


Our model was run with the local sampler (HMM), the type-level sampler (1HMM) and also with the character LM (1HMM-LM).

early work was firmly situtated in the task-based setting of improving generalisation in language models.


2010combined the one class per word type constraint (Brown et al., 1992) in a HMM with a Dirichlet prior to achieve both forms of sparsity.

central to our approach is a new type-based sampling algorithm for hierarchical Pitman-Yor models in which we track fractional table counts.


This model has been popular for language modelling and bilingual word alignment, and an implementation with improved inference called mkcls (Och, 1999) 1 has become a standard part of statistical machine translation systems.

each table at one level is equivalent to a customer at the next deeper level, creating the invariants: where u = t l−1 indicates the unigram backoff context of h. The recursion terminates at the lowest level where the base distribution is static.


In addition to using the MADA features directly, we also develop a set of nine N-gram models (where N=1, 2, and 3) for the nw, pos, and lem features defined in Table 2.

the differences among this model and the other models using lem Table 4 are not statistically significant.


The parser has no local factors, but has the same variables as the joint model and the same features from all three families of link factors ( §3).

• TAG-BIGRAM: There are O(nm 2 ) of such binary factors, each connected to the TAG variables of a pair of neighboring words.


Our model makes use of linguistic information to improve alignment quality.

when the segmentation points are not limited, its performance is much worse.


We find the dis-  tributions over the frequencies of particular errors follow a Zipfian skew across both S&B datasets, with the Arabic being more pronounced (the most frequent error being made 27 times, with 627 errors being made just once) in comparison with the Hebrew (with the most frequent error being made 19 times, and with 856 isolated errors).

the only parameter not present in our model is the probability θ of generating a morpheme as opposed to stopping to gener-ate morphemes (with probability 1 − θ).


Observe that a plausible token is not necessarily induced by any plausible tokenization; in that case, the plausible token is useless to us.

moreover, the advantage of maxPaths over the alternatives increases as k increases, which indicates potential for further improving maxPaths.


The graph G D (s) contains a (directed) edge from a node w 1 , w 1 to a node w 2 , w 2 if w 2 immediately follows w 1 in q ; in other words, G D (s) has an edge from w 1 , w 1 to w 2 , w 2 whenever there exist indices i, j and k, such that w 1 = s [i,j) and w 2 = s [j,k) .

we were not able to do so with Google, as we used remote access via its Java API (Google, 2010); hence, the Google tool is un-aware of our domain, but is rather based on its own statistics (from the World Wide Web).


Moreover, this method is much simpler to implement, since there is no need to train per source language; only one classifier is trained.

our results hold for different training sets, genres, and feature sets.


Finally, an important performance distinction between the two adapted models is the loss in recall exhibited by AP-adapted -its curve is shorter because AP-adapted is very conservative and does not propose many corrections.

while model comparison has not been the goal of the earlier studies, it is quite common to compare systems, even when they are trained on different data sets and use different features.


For example, in Figure 1 an entity linking system should link the name mention Jordan to the entity Michael Jeffrey Jordan and the name mention Bulls to the entity Chicago Bulls.

we can get the training data by collecting all annotated name mentions from the hyperlink data of Wikipedia.


Location error on these is usually quite large.

(2010) do for Facebook, could improve results on the Twitter task considered here.


The value of LEX-MI (PER,d) is then the average difference between the MI of PER and the other NEs.

adding LEX improves performance on PER, but decreases overall performance (line c8).


Other domains have characteristics similar to web queries, e.g., automatically transcribed speech, social communities like Twitter, and SMS.

with these two changes, κ was .54 on KDD-D and .64 on KDD-T.


We began by showing that domain knowledge isn't necessarily required; we learned the MUC-4 template structure with surprising accuracy, learning new semantic roles and several new template structures.

567 documents do not have any templates.


We learn the roles of cluster C by clustering the syntactic relations R C of its words.

we find all role relations and save their arguments (pattern extraction).


Ordering the three sentences differently results in incoherent, cryptic text.

for example, the discourse role transition of "cananea" from S 1 to S 2 is Comp.Arg1→nil.


In Table 3, we report the performance of the linguistic model on the different candidate sets with respect to voice accuracy.

in Table 4, we report the performance of ranking models trained on the different feature subsets introduced in Section 4.


It wasn't until June that it was approved by the Parliament.

for the reverse direction, the meaning representations are mapped to f-structures which can then be mapped to surface strings by the XLE generator (Zarrieß and Kuhn, 2010).


This is a reduction over the many task-specific or highly contextual codes used in the original work.

the question that we are asking instead focuses on how speakers view their authority as a source of information about the topic of the discourse.


It is defined as follows: Kendalls tau seems particularly appropriate for measuring word order differences as the relative ordering words is taken into account.

in this paper we apply the BLEU score as the lexical metric, as it is well known and it measures lexical precision at different n-gram lengths.


Formally, for a generated translation candidate T , the reordering score is calculated as follows.

given a sentence to be translated, we first identify the collocations in the sentence, and then estimate the reordering score according to the translation hypothesis.


A Jump Back (W) operation is only allowed at position Z.

the distance between tj and tj−1 is given as dj = min(|X k − Y l | − 1) ∀ X k ∈ {X1, .


With little to no input uncertainty, the presence of the comma rules out the garden-path analysis of the fronted PP toward the tank, and the surprisal at the main-clause verb is the same across condition (here reflecting only the uncertainty of verb identity for this small grammar).

the PPs involved are overwhelmingly (i) set expressions, such as for example, in essence, and of course, or (ii) manner or temporal adjuncts.


The latter would suffer from the fact that it is not easy to automatically retrieve large bodies of parallel news text with high accuracy.

only the combination of Sup and definition sentence pairs achieved that precision.


As the examples indicate, many of the extracted paraphrases are not specific to definition sentences and seem very reusable.

this strange behavior is due to the scoring by Moses that worked poorly for the data; it gave 1.0 to 82.5% of all the samples, 38.8% of which were incorrect.


To understand collective discourse, we are interested in behavior that happens over a short period of time.

we also plan to build a generation system that employs the Yule model (Yule, 1925) to determine the importance of each aspect (e.g.


We represent each word by its context in the cluster and find the similarity of such contexts.

intuitively, sentences that contain higher ranked words in highly populated communities will have a smaller score.


Our guidelines instructed annotators to extract non-overlapping phrases from each headline as nuggets.

our experiments on two different categories of human-written summaries (headlines and citations) showed that a lot of the diversity seen in human summarization comes from different nuggets that may actually represent the same semantic information (i.e., factoids).


We might also expect permutations that contain ngrams previously seen in the training data to be more natural sounding than other permutations that generate n-grams that have not been seen before.

we describe the set of features in section 3.1, but note that we are free under this formulation to use arbitrary features on the full ordering x of B as well as the head noun H, which we implicitly condition on throughout.


This procedure prioritizes reliable merges over less reliable ones.

we compare the syntactic function baseline and the split-merge system on the auto/auto dataset.


And we acknowledge the support of the National Natural Science Foundation of China under Grant Nos.

every sentence that at least involves one entity mention will be regarded as a candidate event mention, and a randomly selected entity mention from the candidate will be the staring of the whole extraction process.


Within the ACE corpus, there is a strong entity consistency: if one entity mention appears in a type of event, other entity mentions of the same type will appear in similar events, and even use the same word to trigger the events.

but cross-event inference can use a related event "Then he went shopping" within the same document to identify it as a Transport event correctly.


For example, the subtype 1 of "Air" in Table 5 lost the entities of "MiGs" and "enemy planes", but involved "terrorist", "Saddam", etc (See Table 9).

for example, the Population-Center entities only occur in 4 types of event mentions with the conditional probability more than 0.05.


Especially, our worst results also have better performances than cross-event inference.

we manually clustered the subtypes and retry the step-by-step cross-entity inference.


Both approaches, however, generally tackle event recognition and role filler extraction at the same time.

the fourth sentence describes incidental damage to civilian homes following clashes between government forces and guerrillas.


While the types of inferences which may be required is open-ended, certain types come up repeatedly, reflecting the types of slots to be filled: systems would benefit from specialists which are able to reason about times, locations, family relationships, and employment relationships.

sometimes the linking decisions heavily rely on entity profile comparison with Wikipedia infoboxes.


Once we have observed S 1 , however, the chance of (T, L) having a hyponymy relation in-creases.

(2005), a supervised approach was proposed to improve hypernym classification using coordinate terms.


If we use Formula 5.3 to compute the scores of L 1 and L 2 , we can have the following (ignoring IDF for simplicity), Score(L 1 ) √ ; Score(L 2 ) √ One the other hand, if we simply count the total number of supporting sentences, the score of L 2 will be larger.

the chance of observing another supporting sentence becomes larger than before.


The observation state depends on more than one hidden state at each time step in an FHMM.

with gender and number features added, the performance jumped to 0.65.


It is a 2 the available system in fact only includes the testing part.

meanwhile, pos t−1 and op t−1 will jointly generate op t .


Third, the FHMM described here is an integrated system, in contrast with (Haghighi and Klein, 2010).

it may be unfair to compare emPronouns this way with  high-accuracy unsupervised system which reported the best result in Charniak and Elsner (2009).


Early work on automatic dialogue act classification modeled discourse structure with hidden Markov models, experimenting with lexical and prosodic features, and applying the dialogue act model as a constraint to aid in automatic speech recognition (Stolcke et al., 2000).

we focus specifically on occurrences of students' confusion-related facial actions during taskoriented tutorial dialogue.


This difference inevitably requires several modifications to the annotation scheme.

another contribution of this paper is that we take the first step toward answering the question about the performance of existing POS-tagging/chunking techniques on learner data.


Twenty six Japanese college students participated in the project.

our text-collection method is based on writing exercises.


This in itself can affect learner corpus creation much more than one would expect especially when creating a longitudinal learner corpus.

in that annotation scheme and accordingly in ours, errors are tagged using an XML syntax; an error is annotated by tagging a word or phrase that contains it.


Once the texts have been electronically archived, it is relatively easy to maintain and access them.

there are at least two possibilities for tagging: success/NN and success/VB.


We also evaluate Turker translation quality by using them as reference sets to score various submissions to the NIST MT evaluation.

the second method selects the translation that received the best average rank, using the rank labels assigned by other turkers (see 3.3).


It was proved by experiments the low calories eaters mouses had low defending power for flue in ratio.

we solicit redundant translations and edits to them, and automatically select the best output among them.


We distinguish among the possible dependency labels, the head or the tail of the sentence, and whether the aligned word is the governor or the dependent.

we will also carry out experiments on other data sets and for more language pairs.


We also conjecture that syntactic variations were not captured by the n-gram like string-based features in Section 5.2, therefore resulting in BLEU loss, which will be investigated in future work.

this pairwise alignment strategy is prone to spurious insertions and repetitions due to alignment errors such as in Figure 1(a) in which "green" in the third hypothesis is aligned with "forest" in the skeleton.


HM decoding can use lexicalized hypotheses of arbitrary SMT models to derive translation, and a set of component model independent features are used to compute translation confidence.

their method does not change component systems' search spaces.


For example, Chiang (2006) points out that the translation of languages that can stack an unbounded number of clauses in an "inside-out" way (Wu, 1997) provably goes beyond the expressive power of synchronous CFG and TSG.

with translation rules learned from Treebank-style trees, the adjoining tree-to-string system outperforms the baseline system using STSG without significant loss in efficiency.


Our model allows both the source and target phrases of an initial rule with adjoining sites to be discontinuous, which goes beyond the expressive power of synchronous CFG and TSG.

we divide forest rescoring for STAG into three steps: 1. matching, matching STAG rules against the input tree to obtain a TAG derivation forest; 2. conversion, converting the TAG derivation forest into a translation forest; 3. intersection, intersecting the translation forest with an n-gram language model.


Relatively straightforward extensions to the base HMM allow for efficient inference, and agreement between the two models prevents EM from overfitting, even in the absence of harsh penalties.

this project is funded by Microsoft Research.


Finally, when we introduce gappy state phrases of the type s i s j , the number of such phrases is O(m 2 ), since we may choose a start and end point independently.

we are rather strict about this, allowing only 1 → k and k → 1 phrasal alignment edges (or links).


In the multilingual case, it is used to extract translations, that is, pairs of words with the same meaning in source and target corpora.

they will be processed just like aligned comparable documents, that is, we will not take advantage of the structure of the parallel contents to improve accuracy, but will use the exact same approach that we applied for the Wikipedia documents.


Bilingual dictionaries (DIC) allow for precise mappings between words in H and T. To create a large bilingual English-Spanish dictionary we processed and combined the following dictionaries and bilingual resources: -XDXF Dictionaries 7 : 22,486 entries.


We assume N labeled examples {(y 1 ,x 1 ), ..., (y N ,x N )} are available to train the classifier.


First, we establish a reliable partial clustering of units by using synonym choice and then we use these partial clusters as training texts for supervised learning using generic words as features.

formally, our measure of correspondence between the desired clustering and the actual one is computed by first normalizing rows and then computing the weight of the majority diagonal relative to the whole.


Wikification is the task of identifying and linking expressions in text to their referent Wikipedia pages.

this happened mainly because the same mentions were linked to different titles in different sentences, leading to low precision.


Listed is the fraction of identified mentions m whose target disambiguation t is among the top k candidates ranked in descending order of P (t|m).

tAGME is designed for a different setting than ours: extremely short texts, like twitter posts.


Given the mention m and the top-ranked disambiguation t, the linker attempts to decide whether t is indeed the correct disambiguation of m. The linker includes the same features as the ranker, plus additional features we expect to be particularly relevant to the task.

in separate experiments we found that training a linker on out-of-Wikipedia text only increased test set performance by approximately 3 percentage points.


This character-by-character method for segmentation was first proposed in (Xue, 2003), and was then further used in POS tagging in (Ng and Low, 2004).

for both word-based and character-based segmentation, we first match every string of a given sentence with idioms.


As a result, it is very hard to recognize out-of-vocabulary idioms for word segmentation.

we think there are two main reasons: (1) The POS information provided by the local classifier is inaccurate; (2) The structured learning of the sub-word tagger can use real predicted sub-word labels during its decoding time, since this learning algorithm does inference during the training time.


For most Germanic languages like Danish, German, or Swedish, the list of possible linking morphemes is rather small and can be provided manually.

while this works well for English, other languages require additional morphological operations.


This is understandable since the character 䌬 'hall' is so productive that it is impossible for a dictionary to list every word with this character as a suffix.

this means that parsing word structures accurately is possible with a generative model.


As an example, the right tree in Figure 9 has the context free rule "NN → JJf NNf", the head of which should be the rightmost NNf.

for example, the telescopic compound 㦌撥怂惆 'universities, middle schools and primary schools' is in fact composed of three coordinating elements 㦌惆 'university', 撥惆 'middle school' and 怂惆 'primary school'.


The first reason is that if we confine our definition of word segmentation to the identification of word boundaries, then people tend to have divergent opinions as to whether a linguistic unit is a word or not (Sproat et al., 1996).


In the Figure we can see how there is a consistent decrease of the error rate of all measures when the proportion of ties increases (this corresponds to the increase in the fuzziness value).

paulo Rocha, Kiril Ivanov Simov, and Richard F. E.  Sutcliffe.


Since every question has a correct answer, non response is not correct but it is not incorrect either.

the results suggest that the three measures are quite stable, having c@1 and accuracy a lower error rate than UF when the proportion of ties grows.


This is represented in contingency Table 1, where: • n ac : number of questions for which the answer is correct • n aw : number of questions for which the answer is incorrect • n u : number of questions not answered • n: number of questions (n = n ac + n aw + n u ) Let's start studying a simple utility function able to establish the preference order we want: • -1 if question receives an incorrect response • 0 if question is left unanswered • 1 if question receives a correct response Let U(i) be the utility function that returns one of the above values for a given question i.

a comparison of both approaches for answer Validation evaluation is provided in (Rodrigo et al., 2011).


it is often the case that the query question does not have a question body part.


Section 4 describes two models for information need prediction that we use for the experiment.

if the query text contains only one or two words this method will be biased to shorter answer texts (Jeon et al., 2005a).


Broadly, lexical semantic relationships between the "prototype" variable (belonging to the set of seen targets for a frame f ) and the target t i are used as features for frame identification, but since is unobserved, it is summed out both during inference and training.

(If an unlabeled vertex does not have a path to any labeled vertex, this term ensures that its converged marginal will be uniform over all frames.)


In past work, we described an approach where latent variables were used in a probabilistic model to predict frames for unseen targets (Das et al., 2010a).

we followed the same scheme as in §4.1 but with the hyperparameter α = 0.


The number of times φ k was assigned is denoted j k , so that j = K k=1 j k .

since the number of classes is very large, selecting examples at random would result in a relatively low proportion of moves getting accepted, and, consequently, in a slow-mixing Markov chain.


A example question is shown in Figure 3.

many standard inference techniques, such as Gibbs sampling, or the structured mean-field method are unlikely to result in tractable inference.


As would be expected, the sensibility judgements for MFC and model 5 (Tables 1 and 2, Section 3.5) are perfectly anti-correlated (correlation coefficient −1) with the perplexity for these systems in each data set.

in the absence thereof, we resort to several complementary measures, as well as performing an annotation task.


We thus have to restrict ourselves to a subset in order to judge their sensibility.

due to the small sample size, this should be interpreted cautiously.


In this paper, we presented a model based on latent semantics that is able to perform word sense induction as well as disambiguation.

two partitions are thus created over the test set of a target word: a set of automatically generated clusters and a set of gold standard classes.


According to Pedersen (2010), the supervised learning algorithm that underlies this evaluation method tends to converge to the Most Frequent Sense (MFS) baseline, because the number of senses that the classifier assigns to the test instances is rather low.

the quality of the proposed clustering solution is evaluated by comparing the two groupings and measuring their similarity.


For Pros and Cons reviews, the aspects are identified as the frequent noun terms in the reviews, since the aspects are usually noun or noun phrases (Liu, 2009), and it has been shown that simply extracting the frequent noun terms from the Pros and Cons reviews can get high accurate aspect terms (Liu el al., 2005).

more than $156 million online product retail sales have been done in the US market during 2009 (Forrester Research, 2009).


To this end, we here utilize Pros and Cons reviews to train a SVM sentiment classifier.

(2007) proposed a probabilistic topic model to capture the mixture of as-pects and sentiments simultaneously.


A sentence of the raw corpus that contains s words is included twice, once as a sequence of the ⌊s/2⌋ bigrams "w 1 −w 2 w 3 −w 4 w 5 −w 6 .

for a trigram model, this means that we interpolate p KN (w 3 |w 2 ) and p B (w 3 |w 1 w 2 ) on the first backoff level and p KN (w 3 ) and p B (w 3 |w 2 ) on the second backoff level, where p B is the (Brown) class model (see Section 4 for details on p B ).


The key novelty is that we recognize that certain probability estimates are hard to improve based on classes.

the input to the clustering is the vocabulary B i and the cluster training corpus.


Moveover, taking the insights from HMM-LDA that not all the words are content conveying (some of them may just be a result of syntactic requirement), we introduce a dummy functional topic z B for every sentence in the document.

a great amount of effort has recently been made in applying statistical topic models (Hofmann, 1999; to explore word co-occurrence patterns, i.e.


Note that λ>0.5 means that SimFusion relies more on the Chinese-side information than on the English-side information.

e en is the edge set to reflect the relationships between the english sentences.


This means that if pages indexed by Google doubles, then so do the bigrams or trigrams frequencies.

this paper extends previous work to wordto-word selectional preferences by using webscale data.


We address two natural and related questions which some previous studies leave open: Question I: Is there a benefit in incorporating web-derived selectional preference features for statistical dependency parsing, especially for longer dependencies?

in practice, these assumptions are unrealistic in many real applications, such as when processing the heterogeneous genre of web texts.


It uses a conditional MaxEnt model as parse selection component.

interestingly, the 'peaks' in Table 4 (underlined, i.e.


whether we can find a simple yet effective method to determine which model or data is most beneficial for an arbitrary piece of new text.

the union of all three corpora (wsj+genia+brown), which is a standard baseline in domain adaptation (Daumé III, 2007;McClosky et al., 2010).


We experimented with the removal of stopwords, but found no deteriorating effect while keeping them.

by examining the overlap between word-based and topic model-based techniques, we found that despite similar performance their overlap is rather small.


Our analysis confirms tremendous speedups, and shows that for weak models, it can even result in improved accuracy.

the most successful strategy to date for efficient parsing of CCG is to first prune the set of lexical categories considered for each word, using the output of a supertagger, a sequence model over these categories (Bangalore and Joshi, 1999;Clark, 2002).


10 We carried out timing experiments on the best A* parsers for each model (SX and NULL for PCFG and HWDep, respectively), comparing them with our CKY implementation and the agenda-based CKY simulation EXH; we used the same data as in §3.2.

pruning means approximate search: if a lexical category used by the highest probability derivation is pruned, the parser will not find that derivation ( §2).


Adding DET and the so-called φ-features (PERSON, NUMBER, GEN-DER, also shorthanded PNG) in the Greedy setup, yields 1.43% gain over the CORE12 baseline.

7 The terms "feature" and "attribute" are overloaded in the literature.


However, it is likely that from a machine-learning perspective, representing similar categories with the same tag may be useful for learning.

a lemma is a specific word form chosen to represent the lexeme word set; for example, arabic verb lemmas are third person masculine singular perfective.


(2009) and Jiang and Liu (2010) can learn from partially projected trees.

e-GNPPA parser learns around 2-5% more no.


In practice, a class label is deemed more relevant for an instance if the pair is extracted more frequently and by multiple patterns, with the scoring formula: where F req(C, I) is the frequency of extraction of C for the instance I, and Size({P attern(C)}) is the number of unique patterns extracting the class label C for the instance I.

class labels preceding endangered animals already capture the notion of animals or species (first and third labels), or that they are endangered (second label).


In Section 3 we describe the general harvesting procedure, and follow with an examination of the various statistical properties of implicit semantic networks in Section 4, using our implemented harvester to provide illustrative statistics.

similarly, we are interested in understanding how the nodes of the semantic networks connect to each other.


PageRank has revolutionized the whole Internet search society.

the lower-ranked nodes are mostly spelling errors such as Alanis Morisette and Christine Agulera, less known singers such as Buffy Sainte-Marie and Cece Winans, non-American singers such as Alejandro Sanz and France Gall, extractions due to part-of-speech tagging errors such as Billie Celebration, and general terms such as Peter and Sarah.


• Event frames: Event anchors with all their arguments and argument slot names.

each labeled, directed edge in the graph represents a possible dependency between its two endpoints and has an associated score (weight).


Note that although we use the biomedical event domain from the BioNLP'09 shared task to illustrate our work, the core of our approach is almost domain independent.

we bias the anchor detector to favor recall, allowing the parser and reranker to determine which event anchors will ultimately be used.


Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, e.g., a "crime" event can cause a "investigation" event, which can lead to an "arrest" event.

in the BioNLP'09 corpus this leads to the removal of almost 5% of the events, which generate DAGs instead of trees.


We show the effects of each system component in Table 2.

our approach maintains the joint modeling advantage, but our model is built over simple, domain-independent features.


([X-ga Y-bo-da pumjil-I jo-ta]: The quality of X is better than that of Y.)

since this paper focuses on comparisons between different entities, we separate "6) Pseudo" type from "4) Greater or lesser" type.


This will depend on the Markov order of the n-gram model.

intuitively, since the first dimension of the T, T weights is 0 for n-gram arcs and > 0 for backoff arcs, the shortest path will traverse the fewest possible backoff arcs; further, since higherorder backoff arcs cost less in the first dimension of the T, T weights in M , the shortest path will include n-gram arcs at their earliest possible point.


In a typical AL setup, a classifier is trained on a small sample of the data (usually selected randomly), known as the seed examples.

at every selection size, LM sampling results in a higher recall of the rare sense.


While this value is potentially interesting in future applications, here we are only concerned with its sign.

2 Since the accuracy of this LSH routine is a function of b, then if we were able to reduce the online requirements of each counter, we might afford a larger number of projections.


Drift can also be reduced with carefully selected seeds.

pointed out that semantic drift in Espresso has the same root as topic drift (Bharat and Henzinger, 1998) observed with HITS, noting the algorithmic similarity between them.


Possible sources of dialectal text include weblogs, forums, and chat transcripts.

the work of Irvine and Klementiev (2010) is similar to ours in spirit, as they too use Mturk to find annotators with relatively uncommon linguistic skills, to create translation lexicons between English and 42 rare languages.


We set out to develop a POS inventory for Twitter that would be intuitive and informative-while at the same time simple to learn and apply-so as to maximize tagging consistency within and across an-notators.

our contributions are as follows: • we developed a PoS tagset for Twitter, • we manually tagged 1,827 tweets, • we developed features for Twitter PoS tagging and conducted experiments to evaluate them, and • we provide our annotated corpus and trained PoS tagger to the research community.


Due to the different tagsets, we could not apply the pretrained Stanford tagger to our data.

the bulk of this work eschews the standard pipeline of tools which might enable a richer linguistic analysis; such tools are typically trained on newstext and have been shown to perform poorly on Twitter (Finin et al., 2010).


Ideally, CNN returns one point for each cluster, namely the center of each cluster.

intuitively, with relatively simple problems, e.g.


2 We train a semi-supervised condensed nearest neighbor classifier on Sect.

the k most similar points take a weighted vote on the class of x.


Firstly, we obtain an edit operation sequence using the normal DP for edit distance computation.

japanese and foreign titles were split into chunks by middle dots and " ", respectively, and resulting chunks were aligned.


So far, we have relied on the repetition of rhymes, and have made no assumptions about word pronunciations.

• Pronunciation dictionaries are simply not available for many languages.


There have been a number of recent papers on the automated annotation, analysis, or translation of po-etry.

we favor an unsupervised solution that utilizes no external knowledge for several reasons.


In this paper, we explore more linguistically motivated approaches to vandalism detection.

results Table 1 shows the experimental results.


(Bender et al., 2003), our framework requires a relatively small dataset, which may not contain any utterances with multiple dialogue acts.

(Stolcke and Shriberg, 1996), or to align specific words to parts of the formal representation, e.g.


In addition, while theirs is the first end-to-end system for expression extraction with polarities, it is still a sequence labeler, which, by construction, is restricted to use simple local features.

for every pair of opinions in the sentence, we add the pair of polarities: NEG-ATIVE+NEGATIVE.


That is, interlocutors who entrain achieve better communication.

its correlation with score, though not significant, is positive.


We labeled each sentence ending in a question mark (followed optionally by other punctuation) as a question; we also included parentheticals ending in question marks.

the learned predictors for all tasks {w i } are then collected into the columns of a matrix W, on which singular value decomposition USV t = W is performed.


The main idea is to use unlabeled data in both domains to learn linear predictors for many "auxiliary" tasks, which should be somewhat related to the task of interest.

the remaining meetings (in the original development and test divisions; 26k utterances) were used as our test set.


The processing of temporal information (events, time expressions and relations between these entities) is essential for overall comprehension of natural language discourse.

our aim for the FTiB is to provide a corpus of comparable size to TB1.2 (approx.


There is room for improvement, although the system still yields significant gains in total annotation time and quality.


There has been some work on short-term (hourly) temporal analysis of query logs, e.g., Beitzel et al.

notably, they contain a much higher fraction of function words compared to Q-queries, even though they express similar search intent.


While this ensures a legitimate question in well-formed English texts, in queries, we may get "do not call list".

the average click position for Q-queries is consistently higher in rank throughout the time.


The main advantage of this method is that it only uses the intra-language document similarities to identify the alignments across languages.

we find a mapping for each language, such that when the documents are projected into their common subspaces they are more likely to respect the alignment given byΠ.


As similar terms in different languages would have similar occurrence patterns, theoretically, a close representation in the cross-language reduced space should be obtained for terms and sentences that are semantically related.

it still performs well above random chance predictions, which, for the given average of 4 items per ranking, is about 25% for best and worst ranking predictions, and about 8.33% for both.


Our study provides the needed justification for the use of characterlevel metrics in evaluating SMT systems in which Chinese is the target language.

character-level metrics can better capture synonym matches.


Submodular objective functions can represent complex interactions among alignment decisions, and essentially extend the modular (linear) objectives used in the aforementioned approaches.

we evaluated our approaches using the English-French Hansards data from the 2003 NAACL shared task (Mihalcea and Pedersen, 2003).


We therefore suggest: • Replication be adopted as standard practice in MT experimental methodology, especially in reporting results; 7 • Replication of optimization (MERT) and test set evaluation be performed at least three times; more replications may be necessary for experimental manipulations with more subtle effects; • Use of the median system according to a trusted metric when manually analyzing system output; preferably, the median should be determined based on one test set and a second test set should be manually analyzed.

the first system pair contrasts a baseline phrasebased system (Moses) and experimental hierarchical phrase-based system (Hiero), which were constructed from the Chinese-English BtEC corpus (0.7M words), the later of which was decoded with the cdec decoder (Koehn et al., 2007;Chiang, 2007;Dyer et al., 2010).


This research was supported in part by the National Science Foundation through TeraGrid resources provided by Pittsburgh Supercomputing Center under TG-DBS110003; the National Science Foundation under IIS-0713402, IIS-0844507, IIS-0915187, and IIS-0915327; the DARPA GALE program, the U. S. Army Research Laboratory, and the U. S. Army Research Office under contract/grant number W911NF-10-1-0533.

we have also shown that it is possible to control for it with very few replications (Table 2).


f j is associated with a hidden alignment variable a j whose value ranges over the word positions in the corresponding source sentence.

the joint distribution of the Model-1 variables is given by the following generative model 3 : P (E, F, A; t) = s P (e)P (a|e)P (f |a, e; t) (1) In the proposed Bayesian setting, we treat t as a random variable with a prior P (t).


The former two works place nonparametric priors (also known as cache models) on the parameters and utilize Gibbs sampling.

for each source word type e, we assume the prior distribution for t e = t e,1 • • • t e,V f , which is itself a distribution over the target vocabulary, to be a Dirichlet distribution (with its own set of hyperparameters Θ e = θ e,1 • • • θ e,V f ) independent from the priors of other source word types: t e ∼ Dirichlet(t e ; Θ e ) f j |a, e, T ∼ Multinomial(f j ; t ea j ) We choose symmetric Dirichlet priors identically for all source words e with θ e,f = θ = 0.0001 to obtain a sparse Dirichlet prior.


The improvement range is from 2.59 (in Turkish-to-English) up to 2.99 (in English-to-Turkish) BLEU points in travel domain and from 0.16 (in English-to-Czech) up to 0.85 (in English-to-Arabic) BLEU points in news domain.

alignment inference in neither of these works is exactly Bayesian since the alignments are updated by running GIZA++ (Xu et al., 2008) or by local maximization (Nguyen et al., 2010).


We include similar information in our model.

in Table 2, S 0h2 , S 0l2 , S 0r2 and N 0l2 refer to the head of S 0h , the second leftmost modifier and the second rightmost modifier of S 0 , and the second leftmost modifier of N 0 , respectively.


We take the standard split of CTB and use gold segmentation and POS-tags for the input.

our new features to some extent achieved the same effect as their model combination.


2, all the derivation information is embedded in each symbol.

the MH algorithm learns BtSG model parameters efficiently, and it can be applied to our insertion model.


Evaluated by parsing accuracy, these grammars are well below state of the art.

the average sentence lengths for the positive and negative data were 23.9 and 24.7, respectively, for the treebank data  dataset   training  devel.


We propose a new method for query-oriented extractive multi-document summarization.

moreover, graph-based methods for summarization and sentence retrieval are popular (Otterbacher et al., 2005;Varadarajan and Hristidis, 2006;Bosma, 2009).


From Table 2, it can be observed that both QSBP and QSBP(idf) significantly outperforms QSBP(nodist), QSB, WP and the baseline in terms of all evaluation metrics.

nuggets (Voorhees, 2003)) can be covered.


we did not investigate this scale in our study.

pearson's r between ratings given by the same raters first in Image VAS (A) and then in Image VAS (B) was .666 for Adequacy, .593 for Fluency.


An example of a VDS used in NLP is shown in Figure 1.

parametric methods make very strong assumptions about data, including that it is numerical and normally distributed (Siegel, 1957).


The differences in the transcription accuracy for the original and the transformed paragraphs were statically significant at the 0.05 level according to a 2-tailed paired t-test.

4) Pronouns should be used with a lot of wariness.


The mammoth oil spill in the Gulf of Mexico, sparked by the explosion and sinking of a deep-water oil rig, now surrounds the Mississippi River Delta, all but shutting down fisheries.

we count the pattern more than/less than/about/almost ¡integer number¿.


We use a state-of-the-art English IE system as our baseline (Grishman et al.

finally, once all arguments have been assigned, the reportable-event classifier is applied to the potential event mention; if the result is successful, this event mention is reported.


Then they begin an iterative process where they search an unlabeled corpus for patterns given their seeds (with patterns consisting of surrounding tokens, parts-of-speech, syntactic chunks etc.)

figure 3 and figure 4 compare performance on the original TempEval data to performance on the re-annotated version.


Yet in most of these recent evaluations, models are both trained and evaluated on text from the same domain, typically newswire.

the recognition performance of the model is reported in table 1 (column "Basic tempEval Model") for the source and the target domains.


Although, we do not know how many types of relations were involved in the test of (Hoffmann et al., 2010), it is clear that only a small subset of the 5000 relations could have been measured.

such kernels cannot be applied to Wikipedia documents as the entity category, e.g.


Consider the following two sentences from the BioScope corpus: 1) By contrast, {D-mib appears to be uniformly expressed in imaginal discs }.

the 2010 i2b2 NLP Shared task 1 included a track for detection of the assertion status of medical problems (e.g.


As a result, the recall test set is biased away from "true" recall, because it places a higher weight on the "long tail" of instances.

4 Each proposed pattern is applied to the corpus to find a set of hypothesized texts.


Coreference information is provided by BBN's state-of-the-art information extraction 4 Surface text patterns with wild cards are not proposed until the third iteration.

this gives a more accurate indication of the system's ability to find novel instances of a relation.


We performed experiments on Chinese to English MT tasks with the proposed approach.

the boundary words on side A phrases can be unaligned only if they are inside the semantic role constituent.


Given an n-gram language model, we only calculate the scores in windows with the size 2n − 2, centered on the boundary of the substituted phrases.

although the idea is straightforward, we face two problems in practice.


The set of SSR is a subset of the phrase table.

by extracting SRL substitution rules (SSRs) we generate a large set of sentence pairs, and by applying an SVM-based classifier we can filter the corpus, keeping only grammatical sentence pairs.


We also noticed that the new corpus may have very different distribution of words comparing to the baseline corpus.

we propose to build new parallel sentences that convey different information, yet retain correct grammatical and semantic structures.


The parser has access to several labelled examples of the predicate-argument structure of rester, which in many other cases is translated with remain and has the same predicate-argument structure as rester.

the most striking result that we can read from table 1 is that the joint syntactic-semantic learning step results in large improvements, especially for argument labelling, where the F-measure increases from 54% to 65% for the unfiltered data.


The value for each feature is the normalized word string.

we used LIBSVM, a library for support vector machines (SVM), (Chang and Lin, 2001) for multi-class classification with the RBF (Radial Basis Function) kernel.


Our system performed well in the i2b2/VA Challenge, achieving a micro-averaged F 1 -measure of 93.01%.

we performed five-fold cross validation on the training data to measure the impact of each of the four subsets of features explained in Section 3.


Supporting such fluid interactions has become an important topic of virtual human research.

an overview of our approach is depicted in Figure 1.


While LUs were annotated for a poster over the full thread, annotators also marked specific messages in the thread for presence of evidence of the language use.

each example is assigned positive and negative scores, p = n + /N and n = n -/N, where n + is the number of annotators that annotate the example as positive, and nfor the negative.


Therefore we suggest that the recall of our method is close to the value of 0.459.

we have then counted how many of these 1350 errors could be found.


We have then counted how many of these 1350 errors could be found.

the method lacked means for automatic correction of the possibly inconsistent annotations.


Much work has been done on Arabic computational morphology (Al-Sughaiyer and Al-Kharashi, 2004;Soudi et al., 2007;Habash, 2010).

smrž (2007b)'s work contrasting illusory (form) features and functional features inspired our distinction of morphological form and function.


Cardinal numbers could be easily added to improve performance.

furthermore, many of these resources do not map to one another or have restricted licenses.


NU-LEX's first trial demonstrated that it was suitable for general purpose parsing.

of the NU-LEX failures not caused by proper nouns, five of them (20%) were caused by lacking cardinal numbers.


Grateful thanks to Peter Turney, Tara Small, Bridget McInnes, and the reviewers for many wonderful ideas.

there is substantial work on inferring the emotions evoked by colour (Luscher, 1969;Kaya, 2004).


Since many real-world concepts are associated with colour, for example danger with red, linguistic information is often complemented with the use of appropriate colours in information visualization and product marketing.

the use of red to show areas of high crime rate in a visualization is another example of good use of colour to draw emotional response.


Experiments with colour categories have been used both to show that language has an effect on thought (Brown and Lenneberg, 1954;Ratner, 1989) and that it does not (Bornstein, 1985).

(See also the online study by Joe Hallock 2 .)


If we assume independence, then the chance that none of the 5 annotators agrees with each other (majority class size of 1) is 1 × 10/11 × 9/11 × 8/11 × 7/11 = 0.344.

any score above 0.25 shows more than random chance association with a colour.


Other negative emotions-disgust, fear, sadness-go strongest with black.

we requested annotations from US residents only.


Even though the colour options were presented in random order, the order of the most frequently associated colours is identical to the Berlin and Kay order (Section 2:(1)).


So s will have a right branching of monotone concatenation, which contradicts with the definition of LG because right branching of monotone concatenations is prohibited.

when the number of null-aligned words increased, the parsing times for LG and HaG became much longer, caused by the calculation of the large number of spurious derivations.


The results are very similar to the those CKK reported (baseline 25.2, reordered 26.8).

on a news test set, the reordered system performed significantly better than the baseline regardless of the maximum length of phrases.


We see that the choice of data can have a profound effect, nullifying or even reversing the overall result, even when the reordering system remains the same.

this improvement was only apparent with monotonic decoding; when using a distortion model, the difference disappeared.


All the results have been calculated as the average CER of the three users.

ifd is incorrect, it is shown crossed out.


We first briefly describe in section 2 how we apply edit rate computation to the task of atomic paraphrase alignment, and we explain in section 3 how we can inform such a technique with paraphrase candidates extracted by additional techniques.

the authors wish to thank the anonymous reviewers for their useful comments and suggestions.


Second, with the presence of pruning, dynamic programming states that are generated by rules with many target-side terminals are disadvantaged when competing with others in the same bin because of the language model score.

we also investigate a better way to handle targetside terminals during binarization.


The scope of a rule can be calculated by counting the number of adjacent nonterminal pairs and boundary nonterminals.

we could not discern any noticeable difference or trend in terms of BLEU score, decoding speed, or model score when comparing translation results that used grammars that employed nonterminal sharing maximization and ones that did not.


By mining a dictionary and naively incorporating it into a translation system, one can only do slightly better than baseline.

we can use the unlabeled data in the target domain to build a good language model.


(For instance, if all unknown words are names, then copying them verbatim may be sufficient.)

if our dictionary mining system found as-good translations for the words in its list as the (cheating) oracle system, this is how well it would do.


The most obvious way of eliminating problematic unary rules would be converting grammars into Chomsky normal form.

in this section, we present two different ways to handle unary rules.


In this work, the decoding weights for both the baseline and the mixture model are tuned with the Simplex Armijo Downhill algorithm (Zhao and Chen, 2009)  We first experiment with the feature-type-based clustering as described in Section 3.2.

all the features falling in the same feature category/cluster are tied to each other to share the same mixture weights at the upper level as in Eq.


It can be rewritten in the log domain as The individual feature weights λ ki for the i-th feature in cluster k are estimated in the maximumentropy framework as in the baseline model.

we also point out that it is straightforward to combine any of these three.


There are several cases for labeling mismatch, depending on how the polarity changes ( Table 3).

nevertheless, we note there is a surprising large number of flips (24% on average).


The idea is to train on source data like a SVM, but encourage the classification boundary to divide through low density regions in the unlabeled target data.

we argue that it may be 8 The feature normalization in Step 1 is important to ensure that the weight magnitudes are comparable.


The candidates which have been extracted are nested and have different lengths.

as coherent phrase candidates, we extracted all subtrees whose heads are noun.


As mentioned in the previous section, specifying coherent phrases is difficult when using only one source sentence.

then, in decoding, reorderings which violate the zones are restricted.


Then, we specify coherent phrases using the rankings.

these methods did not use document-level context to constrain reorderings.


Factored features allow the language model to capture linguistic patterns at multiple levels of abstraction.

this feature is more precise POS Extended POS Noun SingNoun, PlurNoun Pronoun Sing3PPronoun, OtherPronoun Verb InfVerb, ProgrVerb, SimplePastVerb, PastPartVerb, Sing3PVerb, OtherVerb Table 1: Extended tagset used for the third factor in the proposed discriminative language model.


The fact that spurious word alignments might occur leads to the use of alternative representations for word alignments that allow multiple alignment hypotheses, rather than the 1-best alignment (Venugopal et al., 2009;Mi et al., 2008;Christopher Dyer et al., 2008).

table 3 shows a case where the context based model is more accurate than the regular MSD model.


More recently, the method described in (Liu et al., 2009) produces improvements over the methods above, while reducing the computational cost by using weighted alignment matrices to represent the alignment distribution over each parallel sentence.


In the MSD model, during the phrase extraction, given a source sentence S and a target sentence T , the alignment set A, where a j i is an alignment from i to j, the phrase pair with words in positions between i and j in S, S j i , and n and m in T , T m n , can be classified with one of three orientations with respect to the previous word: • The orientation is monotonous if only the previous word in the source is aligned with the previous word in the target, or, more formally, if a n−1 i−1 ∈ A ∧ a n−1 j+1 / ∈ A.

the relationship between the word alignment quality and the results is not straightforward, and it was shown in (Vilar et al., 2006) that better alignments in terms of F-measure do not always lead to better translation quality.


The development corpus for the BTEC task was the CSTAR03 test set composed by 506 sentences, and the test set was the IWSLT04 test set composed by 500 sentences and 16 references.

it should clearly be discontinuous, since the right alignment for "twin" is "à deux lit".


Running a morphological stemmer (Porter, 1980) on the English side of the parallel data gives a three-way parallel text: for each sentence, we have French f, English e, and stemmed English e ′ .

figure 1 shows the feature weights obtained for the provenance-conditioned features t s ( f | e) in the string-to-string Chinese-English system, trained on newswire and web data.


This finally gives us the following linear program (LP), which characterizes the solution space of the maximum log-likelihood: 4 The two conditions in Eq.

statistical alignment models have become widely used in machine translation, question answering, textual entailment, and non-NLP application areas such as information retrieval (Berger and Lafferty, 1999) and object recognition (Duygulu et al., 2002).


This allows us to train and evaluate a monolingual English classifier for this phenomenon.

conversely, the extraction of social information from texts, and translation from English into languages with a T/V distinction is very difficult.


In the task of bilingual lexicon extraction, two document sets are similar to each other and should be clustered if the combination of the two can complement the content of each single set, which relates to the notion of homogeneity.

our experiments illustrate the well-foundedness of this method and show that the bilingual lexicons obtained from the homogeneous corpus are of better quality than the lexicons obtained with previous approaches.


We have also detected that we are able to boost overall scores if we combine two methods.

p (Z) is a uniform distribution for the BiLDA model, whereas this assumption clearly does not hold for topic models with a non-uniform topic prior.


We successfully retrieve 144, 020 of them, which indicates the recall of our method is about 55.6%.

the word "什恶魔" is deleted and replaced with the correct word "什么" using Pinyin "shenme".


In this paper, as a first step in the project, we focus on content-related criteria.

for our task, however, this is sufficient, since our purpose is to identify, for the person who must choose among them, the report which is most comprehensive in its coverage, or indicate a subset of reports which cover all topics discussed in the collection as a whole 1 .


In this paper, we examine prior techniques that have been used to successfully rank helpfulness for product reviews, and adapt them to the peer-review domain.

not, don't, doesn't), positive and negative words, and so on.


When combined with some generic features, the specialized features improve the model's performance in terms of both r and r s compared to the best performance in Section 4.1 (the baseline).

to enhance the effectiveness of existing peer-review systems, we propose to automatically predict the helpfulness of peer reviews.


For subsequent analysis, we only use the classes Error and OK since Unknown was used extremely rarely and never by all 3 experts for the same sentence.

when evaluating the performance of grammatical error detection systems against human judgments, the judgments for each instance are generally reduced to the single most frequent category: Error or OK.


If one were to report unweighted precision and recall without using any bins-as is the norm-this important qualification would have been harder to discover.

we show that crowdsourcing yields judgments that are as good but without the cost.


(2008) and Felice and Pulman (2008) developed preposition error detection systems, but evaluated on three different corpora using different evaluation measures.

two potential issues arise in that case.


After the rank list is given, the user can adjust the size of the group to best fit his/her needs.

it also demonstrates that our proposed fitness measure using cross-validation works well, and that the two information sources used to build sub-systems can appropriately capture the group characteristics.


However, since the context of an OOV word is unknown and it therefore cannot be assigned to a cluster, OOV words are as much a problem to a context-based class model as to a word model.

in related work, factored language models (Bilmes and Kirchhoff, 2003) were proposed to make use of morphological information in highly inflecting languages such as Finnish (Creutz et al., 2007), Turkish (Creutz et al., 2007;Yuret and Biçici, 2009) and Arabic (Creutz et al., 2007;Vergyri et al., 2004) or compounding languages like German (Berton et al., 1996).


is capital(w) first character of w is an uppercase letter is all capital(w) ∀ c ∈ w : c is an uppercase letter capital character(w) ∃ c ∈ w : c is an uppercase letter appears in lowercase (w) ¬capital Table 1: Predicates of the capitalization and special character groups.

the morphological model reduces perplexity by 81% compared to the KN model.


These support the assumption that the context information are helpful to identify syllable equivalents.

the probability of rule r ∈ R A in Q is estimated by relative frequency where R A is the set of rules rooted at A, and [f r ] −i is the number of times that rule r is used in the tree set t −i .


One way to achieve this goal is to generate a set of candidate segmentations by evaluating the algorithm for multiple values of θ, and then choose the segmentation that minimizes some cost function.

(2010), on this widely-used benchmark corpus are shown in italics.


Such algorithms include MBDP-1 (Brent, 1999), HDP (Goldwater et al., 2009), and WordEnds (Fleck, 2008), each of which is discussed in Section 5.

it is necessary to generate a set of candidate segmentations and select between them according to the MDL principle.


In all of our evaluations, we found that the simple approaches are surprisingly effective in terms of pre-cision, coverage, and redundancy, making them a reasonable choice for an "out of the box" approach for this particular task.

it says that foreign companies have no greater right to compensation -establishing debts at a 1/1 ratio of the dollar to the peso -than Argentine citizens do.


3a then provides a refined entailment model for H, in which the second term specifies the probability that H uc is entailed given that H c is validly entailed and the corresponding lengths: The difficulty in estimating the θ R values is that these are term-level parameters while the RTEtraining entailment annotation is given for the sentence-level.

accordingly we plan to explore relaxing this strict conjunctive behavior through models such as noisy-aND (Pearl, 1988).


The data shows that irony and negative polarity are proportionally distributed regarding the targets involved (Table 2 an almost perfect correlation between them ( 0.99).

when focusing on the debate participants, it can be observed that José Sócrates (C1) censured candidate, and Jerónimo de Sousa ( the least censured one, as shown in Fig.


It indicates many noun features that imply opinions are not directly modified by adjective opinion words.

so for the gold standard, a consensus has to be reached between the two annotators.


It then computes an orientation score for f. A positive word is assigned the semantic orientation (polarity) score of +1, and a negative word is assigned the semantic orientation score of -1.

it also implies a negative opinion about "hump," which indicates a product feature.


They measure the random walk mean hitting time of the given word to the positive set and the negative set.

hu and Liu (2004) use Wordnet synonyms and antonyms to bootstrap from words with known polarity to words with unknown polarity.


The basic idea is that the notion of class attributes will allow generalization to take place across (similar) categories and not just across training examples belonging to the same category.

the taxonomy is defined artificially and is usually very difficult to organize for large scale taxonomy.


As shown in Table 2, one can see that 38 DA types achieve the best performance for the proposed detection model.

, n} converted from sentences according to (7).


All measures are log-scaled and zscore normalized per speaker.

we employ a leave-one-dyad-out cross-validation framework that allows us to determine overall accuracy while highlighting the different characteristics of the dyads.


Finally, as prior researchers have observed, provision of verbal feedback can be viewed as optional.

predicting verbal feedback across languages is challenging due to languagespecific differences, inter-speaker variation, and the relative sparseness and optionality of verbal feedback.


In standard N-Gram models the bias feature corresponds to the unigram distribution.

even the more scalable variant proposed by Mnih and Hinton (2008) is trained on a dataset consisting of only 14M words, also using a vocabulary of around 20000 words.


We will therefore refer to our model as the variable mixture model (VMM).


In this study, we use words, named entities and bigrams to simply represent concepts, and leave the more complex concept extraction for future work.

op jk is a binary variable indicating whether the pair is presented in the summary.


An absolute comparison of the average rewards (rescaled from 0 to 1) of the last 1000 training episodes of each policy shows that greedy improves 'any valid sequence' by 71%, and learnt improves greedy by 29% (these differences are significant at p < 0.01).

there are other linguistic phenomena, such as alignment (Pickering and Garrod, 2004), consistency (Halliday and Hasan, 1976), and variation, which influence people's assessment of discourse (Levelt and Kelter, 1982) and generated output (Belz and Reiter, 2006;Foster and Oberlander, 2006).


Here, the sub-categories of a node correspond to the topics.

in the second phase, the hierarchy is traversed top-down and, at each step, words get assigned to the current node based on whether they can discriminate between the current node's children.


Once the target path c d ∈ H is known, the model reduces to LDA over the set of topics comprising c d .

since the hierarchy is naturally organized in a generic-to-specific fashion, for each node we select words that have the least discriminative power between the node's children.


This is true to some extent, but grammars induced from the WSJ treebank are notorious for over-generation.

we term these single word constituents (SwCs) (see Section 2 for a formal definition).


With the cross-product approach, fewer active states in either child cell leads to fewer grammar access operations.

because the classifier output is imposing hard constraints on the search space of the parser, we may want to choose a tagger operating point that favors precision over recall to avoid over-constraining the downstream parser.


We obtain the best results for Arabic training using labeled data from the Bulgarian treebank, and the best results for Bulgarian training on Portuguese only.

'pnct' from the Danish treebank and 'PUNC' from the Portuguese one.


Ideally, it is better to perform projective parsing for most cases and perform non-projective parsing only when it is needed.

it is desirable to minimize the gap between gold-standard and automatic parses for the best results.


Sister adjunction attaches a tree (or single node) as a sister to another node, and Chomsky-adjunction forms a recursive structure as well, duplicating a node.

we illustrate the basics of TAG-based derivation we are using with examples based on the trees in (2).


For comparison, we chose a sample of 100 nuclei output by DECCA on this same data, and by our judgment the DECCA precision is about 74%, including 15 duplicates.

this is a spurious difference, since the label difference is caused only by the irrelevant presence of an adjunct, and it is clear, without looking at any further structure, that the annotation of qmp $rm Al$yx is identical in (2bc).


The entity Android belongs to the cell-phone domain, not PRG, but appears with positive attributes or contexts because many cell-phones are introduced in advertisements as same as broadcast program.

in our method, the negative examples are chosen far from the positive examples according to the measure of topic similarity.


Theoretically, the number will eventually level off as it gets increasingly harder to generate new Pareto points in a crowded space (Bentley et al., 1978).

life is full of multiple competing objectives.


1 https://github.com/redpony/cdec The great promise of discriminative training for SMT is the possibility to design arbitrarily expressive, complex, or overlapping features in great numbers.

for example, both rules (1) and 2map to the same indicator, namely that a rule is being used that consists of a (NT, term*, NT, term*) pattern on its source side, and an (NT, term*, NT) pattern on its target side.


This is due to stochastic learning on a per-example basis where a weight update on a misclassified example may apply only to a small fraction of data that have been seen before.

instantiating SGD to the following stochastic subgradient leads to the perceptron algorithm for pairwise ranking 3 (Shen and Joshi, 2005): Our baseline algorithm 1 (SDG) scales pairwise ranking to large scale scenarios.


The fluctuation of results is due to sampling training examples from the translation hy-  Table 2: BLEU-4 results for algorithms 1 (SGD), 2 (MixSGD), 3 (IterMixSDG), and 4 (IterSelSGD) on newscommentary (nc) data.

scaling all features to the full training set shows significant improvements for algorithm 3, and especially for algorithm 4, which gains 0.8 BLEU points over tuning 12 features on the development set.


The size of the iPad screen is sufficiently large to allow efficient keyboard entry.

11.4 1.0 10 dB 13.4 1.1 Similar to synchronous HVR, asynchronous HVR also achieved significant performance improvements over the pure ASR systems.


However, since the speech and haptic inputs are provided concurrently, the resulting total input speed for asynchronous HVR is much slower compared to synchronous HVR.

1 since it is independent of W and L. p(O|W) is the acoustic likelihood of the word sequence, W, generating the acoustic feature sequence, O.


This is not surprising since key taps are much quicker to generate compared to handwriting gestures.

a bigram language model with a vocabulary size of 200 words was used for testing.


The number of active tokens per frame also increases from 6260 to 7168 as the SNR drops from the clean condition to 10dB.

this paper proposes a probabilistic framework that encompasses multiple knowledge sources for combining the speech and touch inputs.


Example sentences used for data collection are shown in Table 1.

since each word is represented by a unique PLI (the initial letter) in this work, the PLI model score is given by if N = M , insertions and deletions have to be taken into consideration: where represents an empty token.


It is very useful in expressing the sequences and the hierarchy in picture composition.

when scoring the speech, human raters concern more about how much valid information it contains and irrelevant contents are not taken for penalty.


Second, as the task is speech evaluation, considering the acoustic features may give more information to the automated scoring system.

also, it considers the sequence and the hierarchy information.


We can also treat it as a words FSA, because any word sequence accepted by the words to sensegroups FST is considered to be an integrated composition.

the performance's reduction of the AES system indicates that the similarity is not good enough to extend the FST model.


Although HILDA's bottom-up approach is aimed at building a discourse tree for the full text, it does not explicitly employ different strategies for withinsentence text spans and cross-sentence text spans.

we significantly improve its tree-building step by incorporating our own rich linguistic features.


We significantly improve the performance of HILDA's treebuilding step (introduced in Section 5.1 below) by incorporating rich linguistic features (Section 5.3).

we conduct the following three sub-experiments to explore whether the two classifiers behave differently under different discourse conditions.


At the same time, such superior performance is not due to possible over-fitting on the training data, because we are using significantly fewer features (21,410 for Full and 21,372 for NC) than Hernault et al.

in this work, we aim to develop a textlevel discourse parser.


If we consider all possible combinations of the features listed in Section 5.3, the resulting data space can be horribly high dimensional and extremely sparse.

the evaluation is not biased by the performance on those prevalent classes such as ATTRIBUTION and ELABORATION.


[ Arg2 AltLex After that, the layout had been easy.

to treat explicit and implicit relations alike actually goes beyond annotating them in one pass; it also involves how they are annotated, which we discuss next.


This scheme is shown to be practical and effective in the annotation experiment.

if we apply the same scheme to the Chinese sentence, no discourse relation will be picked out because this is just one comma-separated sentence with no explicit discourse connectives in it.


2009) and scientific corpora (Rosen-Zvi et al., 2004).

this is a useful reality check, since in a debate the moderators are the ones asking questions and literally controlling the topical focus.


These topics are part of a generative model posited to have produced a corpus.

the 2008 Presidential Election Debates Our second dataset contains three annotated presidential debates (Boydstun et al., 2011) between Barack Obama and John McCain and a vice presidential debate between Joe Biden and Sarah Palin.


We applied the following methods to discover topic segmentations in a document: • TextTiling (Hearst, 1997) is one of the earliest generalpurpose topic segmentation algorithms, sliding a fixedwidth window to detect major changes in lexical similarity.

evaluation requires arbitrary thresholding to binarize soft scores.


(2006) where temporal structures were annotated as directed acyclic graphs.

our analysis of the dependency parser errors gives some insights into future research directions.


Barring other knowledge, the learners solely rely on the observed frequencies of unigrams in order to decide which class is most likely.

we extract NER features as sequences of uninterrupted tokens labeled with the same NER tag, ignoring unigrams (since unigrams are already included in the base model).


Finally, we experiment in a balanced data setting, training and testing on the same number of documents from each year.

time expressions are sometimes included, and the last sentence in the original text contains a helpful relative clause: This one clause is more valuable than the rest of the document, allowing us to infer that the document's timestamp is before February, 2000.


's exact method (2011) for Max-Trans-Graph, which utilizes an ILP solver 1 .

for every pair of predicates i, j, an entailment score w ij was learned by training a classifier over distributional similarity features.


We observe that both Exact-graph and TNF substantially outperform No-trans and that TNF's graph quality is only slightly lower than Exact-graph (which is extremely slow).

once c is chosen as the parent of v, choosing v's children in G red is substantially constrained.


In all algorithms that apply an ILP solver, we add a constraint x ij = 1 if i entails j or x ij = 0 if i does not entail j.

intuitively, the topology of an entailment graph is expected to be "tree-like".


We tuned the feature weights on a development set using lattice-based minimum error rate training (MERT) (Macherey et al., The data was pre-processed with packages from the Stanford Arabic parser (Green and Manning, 2010).

translation into English-which accounts for much of the machine translation (MT) literature (Lopez, 2008)-often involves some amount of morpho-syntactic dimensionality reduction.


For comparison, +POS indicates our class-based model trained on the 11 coarse POS tags only (e.g., "Noun").

both Och (1999) and Uszkoreit and Brants (2008) relied on automatically induced classes.


Unification-based Formalisms Agreement rules impose syntactic and semantic constraints on the structure of sentences.

alkuhlani and Habash (2011) have recently started annotating the aTB for animacy, and our model could benefit as more data is released.


Also it incorporates more than 25 times as many tokens as the OPUS corpus.

the example already shows that the assignment obtained by this algorithm is in general not optimal.


Our work uses the same training criterion and is based on the same generative story.

in case of a one-to-one mapping the problem of assigning translation candidates such that the sum of distances is minimal can be solved optimally in polynomial time using the hungarian algorithm (Kuhn, 1955).


We put a Dirichlet prior (α = 1) on the interpolation coefficient λ and learn it during training.

for GIZA++, we used the default settings for word-based alignment, but used the HMM model for character-based alignment to allow for alignment of longer sentences.


This method is insensitive to the existence of competing hypotheses when performing pruning.

4 While suffix arrays allow for efficient calculation of these statistics, storing all co-occurrence counts c(e, f ) is an unrealistic memory burden for larger corpora.


In the formalism presented above, this means that each e i must be included in at most one span, and for each span u = v. Traditionally, these models are run in both directions and combined using heuristics to create many-to-many alignments (Koehn et al., 2003).

in addition, we heuristically prune values for which the conditional probabilities P (e|f ) or P (f |e) are less than some fixed value, which we set to 0.1 for the reported experiments.


We adopt the so called hill climbing framework of Rastrow et al.

this tagger uses a depth- first search lookahead procedure to select the best action at each step, which considers future decisions up to depth d 5 .


Several other related systems work directly from the acoustic signal and many of these do use naturalistic corpora.

in both cases modeling variability improves the accuracy of the learned lexicon over a system that assumes each lexical item has a unique pronunciation.


In this work, the weighted alternatives are then used in a standard (generative) speech recognizer.


In some recent work, discriminative approaches have been proposed, in which an objective more closely related to the task at hand is optimized.

they also tend to cause additional confusability due to the introduction of additional homonyms (Fosler-Lussier et al., 2002).


Similarly, we have P Rc (x ch |HIS) for each child on the right side.

it is very hard to develop higher-order models further in this way.


By processing the auxiliary corpus D ppd with STag ctb , we acquire a new labeled data set D ctb = D ST ag ctb ppd→ctb .

the stacking models can be viewed as data-driven annotation converting models.


4 Structure-based Stacking Each annotation data set alone can yield a predictor that can be taken as a mechanism to produce structured texts.

the goal is to make merged sub-words as large as possible but not overlap with any predicted word produced by the two taggers.


(2009) introduced a bigram HMM model with latent variables (Bigram HMM-LA in the table) for Chinese tagging.

in the tagging phase, the 2m models outputs 2m tagging results, each word is assigned one POS label.


Accordingly, we only keep the subtree dominated by the target VP by pruning from it all the S-nodes along with their subtrees (i.e, all nested sentences are removed).

for example, the verb order can belongs to the two VerbNet classes: -The class 60.1, i.e., order someone to do something as shown in: The Illinois Supreme Court ordered the commission to audit Commonwealth Edison 's construction expenses and refund any unreasonable expenses .


This uses a set of binary SVM classifiers, one for each verb class (frame) i.

it simply confirms that verb classification requires the dependency information between words (i.e., at least the sequential structure information provided by SK).


This process was halted when the total number of states generated by all weighted A* instances exceeded a predefined constant (set to 10, 000).

these new algorithmic contributions were implemented in our system, BIUtEE.


On the other hand, the graph needs the decoder to provide the translation candidates as possible labels, and their posterior probabilities as initial values of various , .

we also saved the spans of the sentences from development and test data, which will be used to create the responding nodes for consensus-based decoding.


2011;Ott et al., 2011; detect fake reviews and reviewers.

this paper proposed the problem of modeling review comments, and presented two models tME and ME-tME to model and to extract topics (aspects) and various comment expressions.


This paper proposes two latent variable models to simultaneously model and extract these key pieces of information.

unlike sentiments and topics in reviews, which are emitted in the same sentence, C-expressions often interleave with topics across sentences and the same comment post may also have multiple types of C-expressions.


We incorporate prior knowledge that we observe in web search query logs as constraints on these latent aspects.

for discriminative methods, we use several named entities, e.g., Movie-Name, Restaurant-Name, Hotel-Name, etc., non-named entities, e.g., Genre, Cuisine, etc., and domain independent dictionaries, e.g., Time, Location, etc.


Yet, it is important because in practice the user often has something in mind to find.

blei and McAuliffe (2007) and Ramage et al.


Seeds may be expressed with mustlinks and cannot-links constraints.

setting the number of topics/aspects in topic models is often tricky as it is difficult to know the 5 http://pages.cs.wisc.edu/~andrzeje/research/df_lda.html exact number of topics that a corpus has.


Using a training data set, we can learn Max-Ent priors.

all these methods do not group extracted aspect terms into categories.


IE systems (Cowie and Lehnert, 1996;Sarawagi, 2008) are trained to extract facts that are stated explicitly in text.

most existing rule learners assume that the training data is largely accurate and complete.


Selecting the set of phrases for each sentence.

such text (e.g., news articles or encyclopedic text) is often only loosely related to an image's specific content and many natural images do not come with associated text for summarization.


Experimental results on the ATIS domain (Dahl et al., 1994) demonstrate that our model outperforms a baseline based on the best derivation and a stateof-the-art discriminative system (Angeli et al., 2010) by a wide margin.

the values of these fields are denver and boston and their type is categorical.


While this may seem daunting, the structures being inferred are actually beneficial to inference.

the unit-wise factors can examine compatibility of settings to the attribute variables for a particular node (for example, the set of topics may be too diverse to represent just a single entity), as well as enforce priors over the tree's breadth and depth.